{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive dout (derivative of loss with respect to outputs) and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch/Layer Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('y_train: ', (49000,))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.769849468192957e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around e-9 or less.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.399100368651805e-11\n",
      "dw error:  9.904211865398145e-11\n",
      "db error:  2.4122867568119087e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around e-10 or less\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be on the order of e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be on the order of e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1: \n",
    "\n",
    "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n",
    "1. Sigmoid\n",
    "2. ReLU\n",
    "3. Leaky ReLU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "1. Sigmoid has the problem -- getting zero (or close to zero) gradient when x is very big or small\n",
    "2. ReLU has the problem --  getting zero (or close to zero) gradient when x is less than zero\n",
    "3. Leaky ReLU has no this problem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward and affine_relu_backward:\n",
      "dx error:  2.299579177309368e-11\n",
      "dw error:  8.162011105764925e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "# Relative error should be around e-10 or less\n",
    "print('Testing affine_relu_forward and affine_relu_backward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.12e-10\n",
      "b1 relative error: 9.83e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "\n",
    "# 我认为，fc_net.py中loss方法中的scores定义是有问题的，scores应该是softmax之后的值，而不是最后一层fc layer的输出值\n",
    "# 当我使用scores=affine2_out时，不会抛出错误提示\n",
    "# 当使用scores=softmax_out时，抛出错误提示\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "# print(loss)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "\n",
    "# 注意: fc_net.py中loss方法，正则项里没有除以batch_size， 按道理正则项应该除以batch_size， 但这样会出错\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "# print(loss)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "# Errors should be around e-7 or less\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.304060\n",
      "(Epoch 0 / 10) train acc: 0.153000; val_acc: 0.150000\n",
      "(Iteration 101 / 4900) loss: 2.317814\n",
      "(Iteration 201 / 4900) loss: 2.450074\n",
      "(Iteration 301 / 4900) loss: 2.354450\n",
      "(Iteration 401 / 4900) loss: 2.166499\n",
      "(Epoch 1 / 10) train acc: 0.325000; val_acc: 0.314000\n",
      "(Iteration 501 / 4900) loss: 2.081820\n",
      "(Iteration 601 / 4900) loss: 2.078286\n",
      "(Iteration 701 / 4900) loss: 1.949573\n",
      "(Iteration 801 / 4900) loss: 2.550756\n",
      "(Iteration 901 / 4900) loss: 2.346297\n",
      "(Epoch 2 / 10) train acc: 0.383000; val_acc: 0.374000\n",
      "(Iteration 1001 / 4900) loss: 1.777913\n",
      "(Iteration 1101 / 4900) loss: 1.966013\n",
      "(Iteration 1201 / 4900) loss: 2.386523\n",
      "(Iteration 1301 / 4900) loss: 1.745973\n",
      "(Iteration 1401 / 4900) loss: 1.746943\n",
      "(Epoch 3 / 10) train acc: 0.405000; val_acc: 0.355000\n",
      "(Iteration 1501 / 4900) loss: 1.901426\n",
      "(Iteration 1601 / 4900) loss: 1.573518\n",
      "(Iteration 1701 / 4900) loss: 1.819675\n",
      "(Iteration 1801 / 4900) loss: 1.816256\n",
      "(Iteration 1901 / 4900) loss: 2.352902\n",
      "(Epoch 4 / 10) train acc: 0.431000; val_acc: 0.394000\n",
      "(Iteration 2001 / 4900) loss: 1.790815\n",
      "(Iteration 2101 / 4900) loss: 1.950221\n",
      "(Iteration 2201 / 4900) loss: 1.618056\n",
      "(Iteration 2301 / 4900) loss: 1.810191\n",
      "(Iteration 2401 / 4900) loss: 1.965022\n",
      "(Epoch 5 / 10) train acc: 0.421000; val_acc: 0.398000\n",
      "(Iteration 2501 / 4900) loss: 1.815968\n",
      "(Iteration 2601 / 4900) loss: 1.981273\n",
      "(Iteration 2701 / 4900) loss: 1.550115\n",
      "(Iteration 2801 / 4900) loss: 1.546178\n",
      "(Iteration 2901 / 4900) loss: 1.811326\n",
      "(Epoch 6 / 10) train acc: 0.414000; val_acc: 0.420000\n",
      "(Iteration 3001 / 4900) loss: 1.939717\n",
      "(Iteration 3101 / 4900) loss: 1.951548\n",
      "(Iteration 3201 / 4900) loss: 1.521317\n",
      "(Iteration 3301 / 4900) loss: 2.077372\n",
      "(Iteration 3401 / 4900) loss: 1.831999\n",
      "(Epoch 7 / 10) train acc: 0.467000; val_acc: 0.407000\n",
      "(Iteration 3501 / 4900) loss: 1.580228\n",
      "(Iteration 3601 / 4900) loss: 1.752803\n",
      "(Iteration 3701 / 4900) loss: 1.715947\n",
      "(Iteration 3801 / 4900) loss: 1.972078\n",
      "(Iteration 3901 / 4900) loss: 1.440752\n",
      "(Epoch 8 / 10) train acc: 0.444000; val_acc: 0.415000\n",
      "(Iteration 4001 / 4900) loss: 1.620342\n",
      "(Iteration 4101 / 4900) loss: 1.492226\n",
      "(Iteration 4201 / 4900) loss: 1.680082\n",
      "(Iteration 4301 / 4900) loss: 1.345732\n",
      "(Iteration 4401 / 4900) loss: 1.884396\n",
      "(Epoch 9 / 10) train acc: 0.481000; val_acc: 0.418000\n",
      "(Iteration 4501 / 4900) loss: 1.337799\n",
      "(Iteration 4601 / 4900) loss: 1.814931\n",
      "(Iteration 4701 / 4900) loss: 1.455977\n",
      "(Iteration 4801 / 4900) loss: 1.465765\n",
      "(Epoch 10 / 10) train acc: 0.480000; val_acc: 0.430000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "solver = Solver(model, data, \n",
    "                update_rule='adam',\n",
    "               optim_config={\n",
    "                   'learning_rate': 1e-03,\n",
    "               },\n",
    "               lr_decay=0.95,\n",
    "               num_epochs=10, batch_size=100,\n",
    "               print_every=100)\n",
    "\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5pRY8nMd2b2rzsgTR5dttD3dXL4wWPrs2r3XuoWcTP5XpaqgnMk6zc/84Dr98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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7ab244390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch/layer normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-7 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "beta1 relative error: 0.00e+00\n",
      "beta2 relative error: 0.00e+00\n",
      "gamma1 relative error: 0.00e+00\n",
      "gamma2 relative error: 0.00e+00\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 3.90e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 2.13e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.57e-10\n",
      "beta1 relative error: 0.00e+00\n",
      "beta2 relative error: 0.00e+00\n",
      "gamma1 relative error: 0.00e+00\n",
      "gamma2 relative error: 0.00e+00\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "  \n",
    "  # Most of the errors should be on the order of e-7 or smaller.   \n",
    "  # NOTE: It is fine however to see an error for W2 on the order of e-5\n",
    "  # for the check when reg = 0.0\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the learning rate and initialization scale to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.302576\n",
      "(Epoch 0 / 20) train acc: 0.240000; val_acc: 0.127000\n",
      "(Epoch 1 / 20) train acc: 0.300000; val_acc: 0.164000\n",
      "(Epoch 2 / 20) train acc: 0.280000; val_acc: 0.156000\n",
      "(Epoch 3 / 20) train acc: 0.280000; val_acc: 0.153000\n",
      "(Epoch 4 / 20) train acc: 0.500000; val_acc: 0.156000\n",
      "(Epoch 5 / 20) train acc: 0.500000; val_acc: 0.132000\n",
      "(Iteration 11 / 40) loss: 1.126671\n",
      "(Epoch 6 / 20) train acc: 0.560000; val_acc: 0.151000\n",
      "(Epoch 7 / 20) train acc: 0.580000; val_acc: 0.175000\n",
      "(Epoch 8 / 20) train acc: 0.560000; val_acc: 0.178000\n",
      "(Epoch 9 / 20) train acc: 0.680000; val_acc: 0.173000\n",
      "(Epoch 10 / 20) train acc: 0.680000; val_acc: 0.174000\n",
      "(Iteration 21 / 40) loss: 0.857221\n",
      "(Epoch 11 / 20) train acc: 0.800000; val_acc: 0.199000\n",
      "(Epoch 12 / 20) train acc: 0.760000; val_acc: 0.206000\n",
      "(Epoch 13 / 20) train acc: 0.840000; val_acc: 0.192000\n",
      "(Epoch 14 / 20) train acc: 0.860000; val_acc: 0.185000\n",
      "(Epoch 15 / 20) train acc: 0.920000; val_acc: 0.192000\n",
      "(Iteration 31 / 40) loss: 0.167474\n",
      "(Epoch 16 / 20) train acc: 0.940000; val_acc: 0.190000\n",
      "(Epoch 17 / 20) train acc: 0.940000; val_acc: 0.198000\n",
      "(Epoch 18 / 20) train acc: 0.980000; val_acc: 0.197000\n",
      "(Epoch 19 / 20) train acc: 0.960000; val_acc: 0.184000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.185000\n"
     ]
    },
    {
     "data": {
      "image/png": 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QSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMLcXFyS1JOm5+pM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7a856c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-3\n",
    "learning_rate = 1e-3\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 116.805391\n",
      "(Epoch 0 / 20) train acc: 0.320000; val_acc: 0.089000\n",
      "(Epoch 1 / 20) train acc: 0.320000; val_acc: 0.111000\n",
      "(Epoch 2 / 20) train acc: 0.540000; val_acc: 0.149000\n",
      "(Epoch 3 / 20) train acc: 0.600000; val_acc: 0.153000\n",
      "(Epoch 4 / 20) train acc: 0.660000; val_acc: 0.158000\n",
      "(Epoch 5 / 20) train acc: 0.720000; val_acc: 0.133000\n",
      "(Iteration 11 / 40) loss: 3.642880\n",
      "(Epoch 6 / 20) train acc: 0.800000; val_acc: 0.113000\n",
      "(Epoch 7 / 20) train acc: 0.900000; val_acc: 0.118000\n",
      "(Epoch 8 / 20) train acc: 0.920000; val_acc: 0.129000\n",
      "(Epoch 9 / 20) train acc: 0.920000; val_acc: 0.124000\n",
      "(Epoch 10 / 20) train acc: 0.940000; val_acc: 0.132000\n",
      "(Iteration 21 / 40) loss: 0.096736\n",
      "(Epoch 11 / 20) train acc: 0.940000; val_acc: 0.121000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.133000\n",
      "(Epoch 13 / 20) train acc: 0.960000; val_acc: 0.121000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.129000\n",
      "(Epoch 15 / 20) train acc: 0.980000; val_acc: 0.130000\n",
      "(Iteration 31 / 40) loss: 0.029759\n",
      "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.141000\n",
      "(Epoch 17 / 20) train acc: 0.960000; val_acc: 0.154000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.148000\n",
      "(Epoch 19 / 20) train acc: 0.980000; val_acc: 0.151000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.153000\n"
     ]
    },
    {
     "data": {
      "image/png": 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sDyP/Mp7XF/lGA+pGvsjn8eU/8ufZaB+6edS20S/leR0H89hmoz9zo36ejWwz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\nGIhwBgAwEOEMAGAgwhkAwED+fz682j+fl3mHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7a836d790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "\n",
    "weight_scale = 1e-1 \n",
    "\n",
    "learning_rate = 2e-3 \n",
    "\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 2: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "\n",
    "1. The 5-layer net become convergent more fastly than the 3-layer net. So I think training 5-layer net is easier.\n",
    " \n",
    "2. I think 3-layer net is more sensitive to the initialization scale. Because 3-layer has few parameters.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "# Should see relative errors around e-8 or less\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.836585\n",
      "(Epoch 0 / 5) train acc: 0.112000; val_acc: 0.134000\n",
      "(Iteration 11 / 200) loss: 2.252931\n",
      "(Iteration 21 / 200) loss: 2.275247\n",
      "(Iteration 31 / 200) loss: 2.038884\n",
      "(Epoch 1 / 5) train acc: 0.318000; val_acc: 0.255000\n",
      "(Iteration 41 / 200) loss: 1.985054\n",
      "(Iteration 51 / 200) loss: 1.863864\n",
      "(Iteration 61 / 200) loss: 2.065046\n",
      "(Iteration 71 / 200) loss: 2.021072\n",
      "(Epoch 2 / 5) train acc: 0.325000; val_acc: 0.257000\n",
      "(Iteration 81 / 200) loss: 1.849133\n",
      "(Iteration 91 / 200) loss: 1.786419\n",
      "(Iteration 101 / 200) loss: 1.875179\n",
      "(Iteration 111 / 200) loss: 1.931323\n",
      "(Epoch 3 / 5) train acc: 0.373000; val_acc: 0.301000\n",
      "(Iteration 121 / 200) loss: 1.790360\n",
      "(Iteration 131 / 200) loss: 1.664227\n",
      "(Iteration 141 / 200) loss: 1.778593\n",
      "(Iteration 151 / 200) loss: 1.755347\n",
      "(Epoch 4 / 5) train acc: 0.401000; val_acc: 0.318000\n",
      "(Iteration 161 / 200) loss: 1.688377\n",
      "(Iteration 171 / 200) loss: 1.770386\n",
      "(Iteration 181 / 200) loss: 1.602883\n",
      "(Iteration 191 / 200) loss: 1.477606\n",
      "(Epoch 5 / 5) train acc: 0.424000; val_acc: 0.322000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.993445\n",
      "(Epoch 0 / 5) train acc: 0.102000; val_acc: 0.092000\n",
      "(Iteration 11 / 200) loss: 2.211721\n",
      "(Iteration 21 / 200) loss: 1.999507\n",
      "(Iteration 31 / 200) loss: 2.002060\n",
      "(Epoch 1 / 5) train acc: 0.316000; val_acc: 0.283000\n",
      "(Iteration 41 / 200) loss: 1.977628\n",
      "(Iteration 51 / 200) loss: 1.905699\n",
      "(Iteration 61 / 200) loss: 1.766211\n",
      "(Iteration 71 / 200) loss: 1.766331\n",
      "(Epoch 2 / 5) train acc: 0.367000; val_acc: 0.344000\n",
      "(Iteration 81 / 200) loss: 1.705270\n",
      "(Iteration 91 / 200) loss: 1.704686\n",
      "(Iteration 101 / 200) loss: 1.797276\n",
      "(Iteration 111 / 200) loss: 1.607611\n",
      "(Epoch 3 / 5) train acc: 0.439000; val_acc: 0.341000\n",
      "(Iteration 121 / 200) loss: 1.580127\n",
      "(Iteration 131 / 200) loss: 1.483003\n",
      "(Iteration 141 / 200) loss: 1.625719\n",
      "(Iteration 151 / 200) loss: 1.554321\n",
      "(Epoch 4 / 5) train acc: 0.484000; val_acc: 0.373000\n",
      "(Iteration 161 / 200) loss: 1.504808\n",
      "(Iteration 171 / 200) loss: 1.158112\n",
      "(Iteration 181 / 200) loss: 1.541918\n",
      "(Iteration 191 / 200) loss: 1.476823\n",
      "(Epoch 5 / 5) train acc: 0.486000; val_acc: 0.357000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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RiIiIiIhInpKihCgmPlGWSxERERERqTcK6CKYM3WMAjgREREREak7GnIpIiIiIiLSoBTQ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7a804c4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.139887467333134e-07\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.914349\n",
      "(Epoch 0 / 5) train acc: 0.166000; val_acc: 0.141000\n",
      "(Iteration 11 / 200) loss: 1.951148\n",
      "(Iteration 21 / 200) loss: 1.874516\n",
      "(Iteration 31 / 200) loss: 1.905338\n",
      "(Epoch 1 / 5) train acc: 0.352000; val_acc: 0.320000\n",
      "(Iteration 41 / 200) loss: 1.728795\n",
      "(Iteration 51 / 200) loss: 1.515695\n",
      "(Iteration 61 / 200) loss: 1.650863\n",
      "(Iteration 71 / 200) loss: 1.653745\n",
      "(Epoch 2 / 5) train acc: 0.417000; val_acc: 0.321000\n",
      "(Iteration 81 / 200) loss: 1.539648\n",
      "(Iteration 91 / 200) loss: 1.495462\n",
      "(Iteration 101 / 200) loss: 1.414676\n",
      "(Iteration 111 / 200) loss: 1.338664\n",
      "(Epoch 3 / 5) train acc: 0.542000; val_acc: 0.347000\n",
      "(Iteration 121 / 200) loss: 1.250263\n",
      "(Iteration 131 / 200) loss: 1.308035\n",
      "(Iteration 141 / 200) loss: 1.463998\n",
      "(Iteration 151 / 200) loss: 1.366098\n",
      "(Epoch 4 / 5) train acc: 0.558000; val_acc: 0.358000\n",
      "(Iteration 161 / 200) loss: 1.362072\n",
      "(Iteration 171 / 200) loss: 1.457668\n",
      "(Iteration 181 / 200) loss: 1.306146\n",
      "(Iteration 191 / 200) loss: 1.283033\n",
      "(Epoch 5 / 5) train acc: 0.573000; val_acc: 0.361000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.712509\n",
      "(Epoch 0 / 5) train acc: 0.121000; val_acc: 0.113000\n",
      "(Iteration 11 / 200) loss: 2.161829\n",
      "(Iteration 21 / 200) loss: 1.901095\n",
      "(Iteration 31 / 200) loss: 1.974049\n",
      "(Epoch 1 / 5) train acc: 0.363000; val_acc: 0.304000\n",
      "(Iteration 41 / 200) loss: 1.878423\n",
      "(Iteration 51 / 200) loss: 1.556557\n",
      "(Iteration 61 / 200) loss: 1.771809\n",
      "(Iteration 71 / 200) loss: 1.705961\n",
      "(Epoch 2 / 5) train acc: 0.424000; val_acc: 0.335000\n",
      "(Iteration 81 / 200) loss: 1.556915\n",
      "(Iteration 91 / 200) loss: 1.703722\n",
      "(Iteration 101 / 200) loss: 1.632552\n",
      "(Iteration 111 / 200) loss: 1.560539\n",
      "(Epoch 3 / 5) train acc: 0.436000; val_acc: 0.342000\n",
      "(Iteration 121 / 200) loss: 1.497558\n",
      "(Iteration 131 / 200) loss: 1.425123\n",
      "(Iteration 141 / 200) loss: 1.582475\n",
      "(Iteration 151 / 200) loss: 1.515850\n",
      "(Epoch 4 / 5) train acc: 0.489000; val_acc: 0.354000\n",
      "(Iteration 161 / 200) loss: 1.470003\n",
      "(Iteration 171 / 200) loss: 1.452281\n",
      "(Iteration 181 / 200) loss: 1.395308\n",
      "(Iteration 191 / 200) loss: 1.449395\n",
      "(Epoch 5 / 5) train acc: 0.512000; val_acc: 0.367000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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vF38BoOTUtxlfVs016yw9j7qDQid5VU1YUtptZuxA9YG4touIiIiISOvRDF08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qrVtV2pEpsZ/gC+vew+NG0pxP72SviKovj27vuAYQPSesfNPLs7a2pQHG3X7M+jHuzb2hdHmxxb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7a813ded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 3:\n",
    "\n",
    "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n",
    "\n",
    "```\n",
    "cache += dw**2\n",
    "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n",
    "```\n",
    "\n",
    "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "in the above update rule: almostly, \n",
    "```          \n",
    "w += - learning_rate\n",
    "```\n",
    "Adam doesn't has the same issue.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_model = FullyConnectedNets()\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# find batch/layer normalization and dropout useful. Store your best model in  #\n",
    "# the best_model variable.                                                     #\n",
    "################################################################################\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test your model!\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
 ],
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   "language": "python",
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   "pygments_lexer": "ipython2",
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